Triple
T2352089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Tate |
E47469
|
entity |
| Predicate | friend |
P8712
|
FINISHED |
| Object | Molly Cartwell |
E259310
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Molly Cartwell | Statement: [John Tate, friend, Molly Cartwell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Molly Cartwell Context triple: [John Tate, friend, Molly Cartwell]
-
A.
Molly Cartwell
chosen
Molly Cartwell is a fictional character best known as the love interest of John Tate in the Halloween horror film series.
-
B.
Molly Greene
Molly Greene is a relatively obscure individual whose specific public notability is not clearly established from the given information.
-
C.
Molly Punderson
Molly Punderson was the wife of American illustrator Norman Rockwell and a figure in his early personal life and career.
-
D.
Molly Stark
Molly Stark was the wife of American Revolutionary War General John Stark, remembered in part through his famous battle cry invoking her name at the Battle of Bennington.
-
E.
Carley Knox
Carley Knox is a sports executive best known for her leadership role in the WNBA’s Minnesota Lynx organization.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88a1b678c8190bce986922ba60ce0 |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abc6f8ff548190b07505310e2bf0b9 |
completed | March 7, 2026, 6:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea884ac4c8190b484db995251c136 |
completed | March 9, 2026, 11:01 a.m. |
Created at: March 4, 2026, 7:54 p.m.